Data-driven control of COVID-19 in buildings: a reinforcement-learning approach

TL;DR

This paper presents a reinforcement learning-based framework for optimizing indoor airflow and disinfectant placement to reduce COVID-19 transmission in buildings, aiming to improve health safety and economic outcomes.

Contribution

It introduces a novel data-driven control framework employing reinforcement learning for infection control through airflow and disinfectant placement in indoor environments.

Findings

Reinforcement learning effectively learns optimal control policies.

Simulation results show rapid convergence to optimal solutions.

Framework offers practical strategies for infection mitigation in buildings.

Abstract

In addition to its public health crisis, COVID-19 pandemic has led to the shutdown and closure of workplaces with an estimated total cost of more than $16 trillion. Given the long hours an average person spends in buildings and indoor environments, this research article proposes data-driven control strategies to design optimal indoor airflow to minimize the exposure of occupants to viral pathogens in built environments. A general control framework is put forward for designing an optimal velocity field and proximal policy optimization, a reinforcement learning algorithm is employed to solve the control problem in a data-driven fashion. The same framework is used for optimal placement of disinfectants to neutralize the viral pathogens as an alternative to the airflow design when the latter is practically infeasible or hard to implement. We show, via simulation experiments, that the control agent learns the optimal policy in both scenarios within a reasonable time. The proposed data-driven control framework in this study will have significant societal and economic benefits by setting the foundation for an improved methodology in designing case-specific infection control guidelines that can be realized by affordable ventilation devices and disinfectants.